Multivariate Linear Models

  • Ronald Christensen
Part of the Springer Texts in Statistics book series (STS)

Abstract

Chapters I, II, and III examine topics in multivariate analysis. Specifically, they discuss multivariate linear models, discriminant analysis, principal components, and factor analysis. The basic ideas behind these subjects are closely related to linear model theory. Multivariate linear models are simply linear models with more than one dependent variable. Discriminant analysis is closely related to both Mahalanobis’s distance (cf. Christensen, 1987, Section XIII.1) and multivariate one-way analysis of variance. Principal components are user-constructed variables which are best linear predictors (cf. Christensen, 1987, Section VI.3) of the original data. Factor analysis has ties to both multivariate linear models and principal components.

Keywords

Profile Analysis Multivariate Normal Distribution Growth Curve Model Full Column Rank Likelihood Ratio Test Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Ronald Christensen
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

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